Today, techniques for object detection or object recognition have been developed and they are applied to a variety of electronic devices. The object detection techniques or the object recognition techniques learn classifiers by using acquired training images, and detect or recognize test images by classifying the test images with the learned classifiers.
FIG. 1 is a drawing exemplarily illustrating a course of classifying objects with a classifier according to a conventional art.
By referring to FIG. 1, positive features are extracted from inputted positive images at a step of S10. The positive images mean images in which an object intended to be detected or recognized exists at a specific size at a specified location. However, in case of test images which are not training images, the object would be placed at any size and at any location in the images. A feature as a value representing a characteristic of an image pixel may be an intensity value of a pixel in accordance with one example embodiment.
Next, the extracted positive features may be converted. Such conversion of the features represents the change in characteristics of the features differently. For example, a classification system may create new features by converting an input image with RGB channels through Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Canonical Correlation Analysis (CCA).
Negative images may be acquired from a negative image pool 200 at a step of S30. Herein, the negative images may be images at random sizes in which there is no object that is intended to be detected or recognized. Hard negative images may be selected from the acquired negative images. A hard negative image represents a negative image which is mistaken or is highly likely to be mistaken for a positive image by the classifier.
Just like the positive images, hard negative features may be extracted from the hard negative images at a step of S40. The extracted hard negative features may be converted.
The classifier may classify inputted images by using at least one classification tree at steps of S20 and S50.
In addition, a bootstrapping process capable of finding the hard negative images which are more difficult to be segregated from the positive images in the negative image pool 200 may be performed at a step of S60.
A detector is configured by including at least one classifier and such a detector or a classifier is required to be learned by using training images.
But the conventional detector has drawbacks in that it requires a longer time to classify because respective feature IDs and thresholds for respective nodes of a classification tree are different from each other, and it has a lower efficiency because of a large model size.